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Unified framework for automated iris segmentation using distantly acquired face images.

Chun-Wei Tan¹, Ajay Kumar

¹Hong Kong Polytechnic University, Kowloon, Hong Kong. cscwtan@comp.polyu.edu.hk

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|May 23, 2012

Summary

This study introduces a novel iris segmentation framework for robust human identification. The new method significantly reduces segmentation errors in iris images, enhancing biometric security applications.

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Area of Science:

Biometrics
Computer Vision
Pattern Recognition

Background:

Accurate iris segmentation is crucial for reliable remote human identification in civilian and surveillance contexts.
Existing iris segmentation algorithms struggle with image variations from near-infrared or visible illumination.

Purpose of the Study:

To develop a robust iris segmentation framework for accurate iris region extraction from images acquired under varying illumination.
To improve the performance of automated iris biometrics through enhanced segmentation.

Main Methods:

A novel framework exploiting higher-order local pixel dependencies for iris/non-iris classification.
Integration of face and eye detection modules for automated localization of the eye region.
Development of robust post-processing algorithms to mitigate misclassification noise.

Main Results:

Significant reductions in average segmentation errors: 47.5% on UBIRIS.v2, 34.1% on FRGC, and 32.6% on CASIA.v4.
Demonstrated robustness across different publicly available at-a-distance iris databases.
Validation of the approach through successful recognition experiments.

Conclusions:

The proposed iris segmentation framework offers superior performance compared to existing methods.
The framework's robustness and accuracy are beneficial for real-world biometric identification systems.
Further improvements in iris biometrics are achievable through advanced segmentation techniques.